%A Wei Wang
%A Florent Masseglia
%A Thomas Guyet
%A Rene Quiniou
%A Marie-Odile Cordier
%T A General Framework for Adaptive and Online Detection of Web Attacks
%X Detection of web attacks is an important issue in current
defense-in-depth security framework. In this paper, we pro-
pose a novel general framework for adaptive and online de-
tection of web attacks. The general framework can be based
on any online clustering methods. A detection model based
on the framework is able to learn online and deal with ?con-
cept drift? in web audit data streams. Str-DBSCAN that we
extended DBSCAN [1] to streaming data as well as StrAP
[3] are both used to validate the framework. The detec-
tion model based on the framework automatically labels
the web audit data and adapts to normal behavior changes
while identifies attacks through dynamical clustering of the
streaming data. A very large size of real HTTP Log data col-
lected in our institute is used to validate the framework and
the model. The preliminary testing results demonstrated its
effectiveness.

%C Madrid, Spain
%D 2009
%P 1141-1141
%L www2009151